QSAR & predictive modelling.

Interpretable machine-learning models for ADMET, target activity and toxicity prediction. We build, benchmark and deploy them with our SIBILA AutoML framework, and expose them as target-specific web servers for diabetes, obesity, anti-aging and natural compounds.

What we work on

Concrete predictive modelling problems we solve for in-house programs and external partners.

  • ADMET prediction — absorption, distribution, metabolism, excretion and toxicity models for hit triage.
  • Target activity prediction — QSAR models trained on curated datasets, with applicability domain analysis.
  • Toxicity and safety — hERG, cytotoxicity and tissue-specific risk models.
  • Interpretability — every prediction is delivered with feature attribution (SHAP, LIME, descriptor importance), not as a black box.
  • Target-specific servers — public web tools for anti-diabetic, anti-obesity, anti-aging and antioxidant activity.

Tools we use

  • SIBILAAutoML platform for interpretable predictive models.
  • DIA-DBDiabetes drug prediction by similarity and inverse virtual screening.
  • OBE-DBAnti-obesity drug prediction.
  • AntiAge-DBNatural cosmetic anti-aging compound prediction (consolidating the former NC-DB resource).
See all tools →

Applications & target areas

Where interpretable QSAR is delivering value for our partners today.

Pharma R&D

Early ADMET and toxicity filtering of compound libraries before in vitro validation.

Metabolic disease

Anti-diabetic and anti-obesity activity prediction for repurposing and natural products.

Cosmetics

Anti-aging ingredient screening through AntiAge-DB and custom QSAR models.

Clinical & environmental

Cardiovascular risk, hospital-readmission and drought-monitoring models built on the same SIBILA stack.

Selected papers

Reference publications underpinning this line.

TopicReference
SIBILA — interpretable AutoML platform10.3390/ai5040116
DIA-DB — diabetes drug prediction server10.1021/acs.jcim.0c00107
OBE-DB — anti-obesity drug predictionpreprint 10.1101/2025.04.10.648110
AntiAge-DB — cosmetic anti-aging predictions10.3390/antiox11112268
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Prof. Horacio Pérez-Sánchez · hperez@ucam.edu